--- license: cc-by-sa-4.0 datasets: - procesaur/ZNANJE - procesaur/STARS - procesaur/Vikipedija - procesaur/Vikizvornik - jerteh/SrpELTeC - procesaur/kisobran language: - sr - hr base_model: - FacebookAI/xlm-roberta-base ---

XLMali

Вишејезични модел, 279 милиона параметара

Обучаван над корпусима српског и српскохрватског језика - 20 милијарди речи

Једнака подршка уноса на ћирилици и латиници!

Multilingual model, 279 million parameters

Trained on Serbian and Serbo-Croatian corpora - 20 billion words

Equal support for Cyrillic and Latin input!
```python >>> from transformers import pipeline >>> unmasker = pipeline('fill-mask', model='te-sla/teslaXLM') >>> unmasker("Kada bi čovek znao gde će pasti on bi.") ``` ```python >>> from transformers import AutoTokenizer, AutoModelForMaskedLM >>> from torch import LongTensor, no_grad >>> from scipy import spatial >>> tokenizer = AutoTokenizer.from_pretrained('te-sla/teslaXLM') >>> model = AutoModelForMaskedLM.from_pretrained('te-sla/teslaXLM', output_hidden_states=True) >>> x = " pas" >>> y = " mačka" >>> z = " svemir" >>> tensor_x = LongTensor(tokenizer.encode(x, add_special_tokens=False)).unsqueeze(0) >>> tensor_y = LongTensor(tokenizer.encode(y, add_special_tokens=False)).unsqueeze(0) >>> tensor_z = LongTensor(tokenizer.encode(z, add_special_tokens=False)).unsqueeze(0) >>> model.eval() >>> with no_grad(): >>> vektor_x = model(input_ids=tensor_x).hidden_states[-1].squeeze() >>> vektor_y = model(input_ids=tensor_y).hidden_states[-1].squeeze() >>> vektor_z = model(input_ids=tensor_z).hidden_states[-1].squeeze() >>> print(spatial.distance.cosine(vektor_x, vektor_y)) >>> print(spatial.distance.cosine(vektor_x, vektor_z)) ```
Евалуација XLMR-base модела за српски језик
Serbian XLMR-base models evaluation results
Author
Mihailo Škorić
@procesaur
Author
Saša Petalinkar
@tanor
Computation
TESLA project
@te-sla

## Cit. ```bibtex @inproceedings{skoricxlm, author = {Mihailo Škorić, Saša Petalinkar}, title = {New XLM-R-based language models for Serbian and Serbo-Croatian}, booktitle = {ARTIFICAL INTELLIGENCE CONFERENCE}, year = {2024}, address = {Belgrade} publisher = {SASA, Belgrade}, url = {} } ```

Истраживање jе спроведено уз подршку Фонда за науку Републике Србиjе, #7276, Text Embeddings – Serbian Language Applications – TESLA

This research was supported by the Science Fund of the Republic of Serbia, #7276, Text Embeddings - Serbian Language Applications - TESLA